This time we are going to discuss the influence of two basic variables on the quality of SVM classifier. They are called hyperparameters to distinguish them from the parameters optimized in a machine learning procedures. Two previous posts introduced Support Vector Machine itself and data preprocessing for this classifier. As in other Machine Learning techniques there is also a need to properly adjust some system variables to find the best model for our needs. Here, we will focus on description of complexity parameter and gamma parameter from the Gaussian kernel. In the next article we will find an optimum SVM model for the foreground/background estimation problem in Flover project using model validation techniques.More SVM model selection - how to adjust all these knobs pt. 1